Mastering the Wizardry of CS and Edu ++

There has been a dramatic lag in my contribution to this site of late, loads of new projects, full time work plus sporadic thesis guilt conspired to prevent a posting until now.

A few things I’m up to:

Girl Develop IT Code and Coffee (Feburary 5th)

Radio Show (Sundays, 9-10PM)

ArtSec Demo Projection

ArtSec Demo Projection

Conversely, my internet activities are pretty consistent off of this site. Look for my posts here (Control Group, Girl Develop It), some comments in the Google group (Art Sec), and check out my archived radio shows here.  I’ve made a few videos with cool prototypes in physical computing as well, including LED blink programs. Most of the photos in this post cull from recent events and otherwise awesome goings on. With that, explaining, I’ll proceed to some more substantive commentary.

For this post I want to focus on a consistent preoccupation of mine, one that I think bridges all of the above enumerated activities: education. As a librarian, it’s increasingly hard to abandon the idea of research for a purpose, which is pretty ubiquitous in education, the idea of consistent and independent edification. I’ve been collecting articles and thinking about this for a while.

Human Face of Big Data

Human Face of Big Data

At Bloomberg’s World IA Day (February 9th), Rich Smolan talked about positive impact of data analysis en masse, for building the potential of networked intelligence, for translating ugly data into meaningful information and contributing to the global nervous system that the internet provides. With large amounts of collective data about our population and behaviors, we are actively architecting an engine for understanding our world. Education in how to process information of this complexity and quantity is key. So, a consistent topic of discussion was what is the right balance of education in Information Architecture?

But perhaps more pertinent and consistent, the question of how soon to education and in what sequence of curricula we might begin teaching about big data and programming. Should we begin by reinforcing mathematics and logic because they are the foundation of careful thought in computation? Should we jump to scripting, robots, and physical computing because they are the jazzy IRL exactions of programming? Should we leave it to students and promote programming and data fluency in general?

Where should we start? Of late I’ve been engaged in some peripheral education exercises, wrapping up as a metadata T.A. at Pratt left me with a nostalgia for teaching and the above event list is just a catalog of my tangential pursuits in

Education and Outreach: More technicolor data plz!

Education and Outreach: More technicolor data plz!

information nerdery. It made me think about what I might be competent to teach and what I would want to teach, and part of my work with Girl Develop It has only continuously affirmed that I want to work with data and I want to teach people how to use it for the types of progressive applications that Smolan talks about in his The Human Face of Big Data. Programming is inching toward ubiquity even in obligatory curricula, and even a more basic understanding of balance in structuring and formatting data for consumption will soon be a prerequisite for a high school curriculum in CS. This was a topic that I revisited a few weeks ago when I taught a class at the Academy for Software Engineering, a new Manhattan High School focused on teaching programming in tandem with typical coursework. Part of their Functions and Data Analysis curriculum, the class was about teaching 9th graders how to approach the ubiquity and enormity of data output that they unconsciously contribute to on the daily. Most of the class was just straight up Big Data, but understanding how to structure data, how to architect and organize information for usability is an interdisciplinary skill worth cultivating at all educational levels, whether professional (as at World IA Day), collegiate, or early educational.

Check out the presentation here: CGBigData-AFSE-1.3.13

Likewise, at this month’s Open Data Day, I focused on building out a series of collaborative iPython Notebooks in PiCloud to create the skeleton of a collaborative programming curriculum in Python for Girl Develop It. Ideally, the notebooks would allow me to segment blocks of code and wrap them in a user friendly set of READ.ME-like comments in markdown. I could then share the notebooks with students and collaborators who could run the code blocks individually and process the interactive lesson plan before them as a UI-friendly literate programming environment. Developing literacy  at the expense of obscurity here is key to encouraging new programmers.

Working hard at being a nerd

Working hard at being a nerd

So, in considering all of the above, I naturally thought about my own habits of continuous education since college, about how I’ve supplemented my traditional curriculum to afford forays into CS and programming when that was not/never my primary program of study. And also about who encouraged this study and what kept me going.

Were I teaching a college course in information architecture, I would teach my students to…

  • pursue independent study (rare book school/hacker school, code.org, codeacademy, )
  • mentor and expect reciprocal mentorship from your superiors
Collabo-nerding

Collabo-nerding

  • participate in regular portfolio critique as an exercise
  • learn something outside of the nebulous field you participate in professionally, because those soundbites of even abbreviated variety in intelligence are so surprisingly significant for persuade
  • learn a really lean/agile process (aside from the  learning more about accessibility)
  • design for extremity to outperform use cases, you will never be disappointed and can scale this practice with experience

The reality is that most brilliant things that develop from your education after age 21 are probably things you designed and built yourself. Honing your skill set through regular exercises outside of your traditional workflows (extra classes, hackathons, meetups) are

Pitching ideas at Open Data Day NYC

Pitching ideas at Open Data Day NYC

an essential part of the continuous learning process. One of the unspoken (or maybe spoken) refrains of graduate school is that you don’t really need to go to grad school (something you realize inevitably and only while you’re there). Most education is just a framework for realizing your own potential; the older you are the more apparent this becomes, the more you must make independent effort to educate yourself outside of an obligatory education track. Encouragement can help (see code.org video or the Take the Pledge series from CS Ed Week – look out for my cameo!):

As a concluding point, I used to think that people who defended “liberal arts education” where trying to justify their own youthful unprofessional orientation, but I have come to recognize that the peculiar demands of most professions are irrelevant if you fail to communicate and complicate your own ideas. This is something that liberal arts teach you, how to build on your own concepts and inform or affirm them with research and critical theory. Intelligent people are remarkable problem solvers. If you train an intelligent person to approach your problem set, he will make progress toward a solution; diversity in education enriches this capacity. The answer to questions of more creativity and a more informed approach to architecture anchors in an independent and continuous education.

Advertisements
Tagged , ,
%d bloggers like this: